Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations3150
Missing cells0
Missing cells (%)0.0%
Duplicate rows165
Duplicate rows (%)5.2%
Total size in memory234.5 KiB
Average record size in memory76.2 B

Variable types

Numeric8
Categorical5

Alerts

Dataset has 165 (5.2%) duplicate rowsDuplicates
Call Failure is highly overall correlated with Charge Amount and 2 other fieldsHigh correlation
Charge Amount is highly overall correlated with Call FailureHigh correlation
Churn is highly overall correlated with ComplainsHigh correlation
Complains is highly overall correlated with ChurnHigh correlation
Customer Value is highly overall correlated with Distinct Called Numbers and 3 other fieldsHigh correlation
Distinct Called Numbers is highly overall correlated with Call Failure and 3 other fieldsHigh correlation
Frequency of SMS is highly overall correlated with Customer ValueHigh correlation
Frequency of use is highly overall correlated with Call Failure and 4 other fieldsHigh correlation
Seconds of Use is highly overall correlated with Customer Value and 3 other fieldsHigh correlation
Status is highly overall correlated with Frequency of use and 1 other fieldsHigh correlation
Complains is highly imbalanced (61.0%) Imbalance
Tariff Plan is highly imbalanced (60.6%) Imbalance
Call Failure has 702 (22.3%) zeros Zeros
Charge Amount has 1768 (56.1%) zeros Zeros
Seconds of Use has 154 (4.9%) zeros Zeros
Frequency of use has 154 (4.9%) zeros Zeros
Frequency of SMS has 603 (19.1%) zeros Zeros
Distinct Called Numbers has 154 (4.9%) zeros Zeros
Customer Value has 132 (4.2%) zeros Zeros

Reproduction

Analysis started2025-03-15 16:28:20.934829
Analysis finished2025-03-15 16:28:24.871946
Duration3.94 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Call Failure
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6279365
Minimum0
Maximum36
Zeros702
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:24.922249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q312
95-th percentile22
Maximum36
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2638856
Coefficient of variation (CV)0.95227399
Kurtosis0.90682067
Mean7.6279365
Median Absolute Deviation (MAD)5
Skewness1.0897518
Sum24028
Variance52.764034
MonotonicityNot monotonic
2025-03-15T21:58:24.992845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 702
22.3%
5 244
 
7.7%
7 166
 
5.3%
6 161
 
5.1%
8 156
 
5.0%
9 149
 
4.7%
3 141
 
4.5%
2 137
 
4.3%
4 133
 
4.2%
11 125
 
4.0%
Other values (27) 1036
32.9%
ValueCountFrequency (%)
0 702
22.3%
1 121
 
3.8%
2 137
 
4.3%
3 141
 
4.5%
4 133
 
4.2%
5 244
 
7.7%
6 161
 
5.1%
7 166
 
5.3%
8 156
 
5.0%
9 149
 
4.7%
ValueCountFrequency (%)
36 2
 
0.1%
35 2
 
0.1%
34 3
 
0.1%
33 3
 
0.1%
32 8
0.3%
31 6
 
0.2%
30 16
0.5%
29 7
0.2%
28 17
0.5%
27 13
0.4%

Complains
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
2909 
1
 
241

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Length

2025-03-15T21:58:25.058791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T21:58:25.111431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Subscription Length
Real number (ℝ)

Distinct45
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.541905
Minimum3
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.167468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q130
median35
Q338
95-th percentile42
Maximum47
Range44
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.5734821
Coefficient of variation (CV)0.26345975
Kurtosis1.2158424
Mean32.541905
Median Absolute Deviation (MAD)4
Skewness-1.300015
Sum102507
Variance73.504595
MonotonicityNot monotonic
2025-03-15T21:58:25.231710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36 276
 
8.8%
38 258
 
8.2%
37 229
 
7.3%
35 228
 
7.2%
34 201
 
6.4%
39 201
 
6.4%
40 186
 
5.9%
33 152
 
4.8%
32 121
 
3.8%
41 110
 
3.5%
Other values (35) 1188
37.7%
ValueCountFrequency (%)
3 8
 
0.3%
4 4
 
0.1%
5 6
 
0.2%
6 8
 
0.3%
7 19
0.6%
8 12
0.4%
9 22
0.7%
10 16
0.5%
11 26
0.8%
12 19
0.6%
ValueCountFrequency (%)
47 1
 
< 0.1%
46 13
 
0.4%
45 23
 
0.7%
44 44
 
1.4%
43 56
 
1.8%
42 80
 
2.5%
41 110
3.5%
40 186
5.9%
39 201
6.4%
38 258
8.2%

Charge Amount
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94285714
Minimum0
Maximum10
Zeros1768
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.288341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5210719
Coefficient of variation (CV)1.6132581
Kurtosis8.8543583
Mean0.94285714
Median Absolute Deviation (MAD)0
Skewness2.5848682
Sum2970
Variance2.3136597
MonotonicityNot monotonic
2025-03-15T21:58:25.343951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1768
56.1%
1 617
 
19.6%
2 395
 
12.5%
3 199
 
6.3%
4 76
 
2.4%
5 30
 
1.0%
8 19
 
0.6%
9 14
 
0.4%
7 14
 
0.4%
6 11
 
0.3%
ValueCountFrequency (%)
0 1768
56.1%
1 617
 
19.6%
2 395
 
12.5%
3 199
 
6.3%
4 76
 
2.4%
5 30
 
1.0%
6 11
 
0.3%
7 14
 
0.4%
8 19
 
0.6%
9 14
 
0.4%
ValueCountFrequency (%)
10 7
 
0.2%
9 14
 
0.4%
8 19
 
0.6%
7 14
 
0.4%
6 11
 
0.3%
5 30
 
1.0%
4 76
 
2.4%
3 199
 
6.3%
2 395
12.5%
1 617
19.6%

Seconds of Use
Real number (ℝ)

High correlation  Zeros 

Distinct1756
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4472.4597
Minimum0
Maximum17090
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.408277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.5
Q11391.25
median2990
Q36478.25
95-th percentile15020.5
Maximum17090
Range17090
Interquartile range (IQR)5087

Descriptive statistics

Standard deviation4197.9087
Coefficient of variation (CV)0.93861297
Kurtosis0.99367573
Mean4472.4597
Median Absolute Deviation (MAD)1996
Skewness1.3219429
Sum14088248
Variance17622437
MonotonicityNot monotonic
2025-03-15T21:58:25.481294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
305 37
 
1.2%
710 9
 
0.3%
1015 9
 
0.3%
1973 9
 
0.3%
2088 9
 
0.3%
1360 8
 
0.3%
825 8
 
0.3%
955 8
 
0.3%
1180 8
 
0.3%
Other values (1746) 2891
91.8%
ValueCountFrequency (%)
0 154
4.9%
8 1
 
< 0.1%
13 1
 
< 0.1%
33 1
 
< 0.1%
50 1
 
< 0.1%
60 1
 
< 0.1%
73 1
 
< 0.1%
80 1
 
< 0.1%
88 1
 
< 0.1%
93 2
 
0.1%
ValueCountFrequency (%)
17090 1
< 0.1%
16980 1
< 0.1%
16785 1
< 0.1%
16675 1
< 0.1%
16640 1
< 0.1%
16570 1
< 0.1%
16560 1
< 0.1%
16500 1
< 0.1%
16495 1
< 0.1%
16480 1
< 0.1%

Frequency of use
Real number (ℝ)

High correlation  Zeros 

Distinct242
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.460635
Minimum0
Maximum255
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.554296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q127
median54
Q395
95-th percentile184.55
Maximum255
Range255
Interquartile range (IQR)68

Descriptive statistics

Standard deviation57.413308
Coefficient of variation (CV)0.82655893
Kurtosis0.82012484
Mean69.460635
Median Absolute Deviation (MAD)33
Skewness1.1441664
Sum218801
Variance3296.2879
MonotonicityNot monotonic
2025-03-15T21:58:25.625864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
6 49
 
1.6%
44 38
 
1.2%
39 37
 
1.2%
41 35
 
1.1%
33 33
 
1.0%
36 33
 
1.0%
25 32
 
1.0%
47 32
 
1.0%
45 32
 
1.0%
Other values (232) 2675
84.9%
ValueCountFrequency (%)
0 154
4.9%
1 9
 
0.3%
2 15
 
0.5%
3 4
 
0.1%
4 23
 
0.7%
5 15
 
0.5%
6 49
 
1.6%
7 19
 
0.6%
8 25
 
0.8%
9 16
 
0.5%
ValueCountFrequency (%)
255 1
 
< 0.1%
254 2
 
0.1%
252 1
 
< 0.1%
250 2
 
0.1%
249 1
 
< 0.1%
248 2
 
0.1%
247 1
 
< 0.1%
246 2
 
0.1%
245 1
 
< 0.1%
244 5
0.2%

Frequency of SMS
Real number (ℝ)

High correlation  Zeros 

Distinct405
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.174921
Minimum0
Maximum522
Zeros603
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.695535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median21
Q387
95-th percentile356.55
Maximum522
Range522
Interquartile range (IQR)81

Descriptive statistics

Standard deviation112.23756
Coefficient of variation (CV)1.5338255
Kurtosis3.2585401
Mean73.174921
Median Absolute Deviation (MAD)21
Skewness1.9741418
Sum230501
Variance12597.27
MonotonicityNot monotonic
2025-03-15T21:58:25.769081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 603
 
19.1%
7 194
 
6.2%
9 54
 
1.7%
15 54
 
1.7%
8 54
 
1.7%
16 51
 
1.6%
17 47
 
1.5%
10 44
 
1.4%
12 42
 
1.3%
1 41
 
1.3%
Other values (395) 1966
62.4%
ValueCountFrequency (%)
0 603
19.1%
1 41
 
1.3%
2 39
 
1.2%
3 32
 
1.0%
4 30
 
1.0%
5 29
 
0.9%
6 26
 
0.8%
7 194
 
6.2%
8 54
 
1.7%
9 54
 
1.7%
ValueCountFrequency (%)
522 1
 
< 0.1%
515 1
 
< 0.1%
511 1
 
< 0.1%
508 1
 
< 0.1%
505 1
 
< 0.1%
504 1
 
< 0.1%
501 1
 
< 0.1%
500 1
 
< 0.1%
499 1
 
< 0.1%
498 3
0.1%

Distinct Called Numbers
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.509841
Minimum0
Maximum97
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:25.840936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median21
Q334
95-th percentile51
Maximum97
Range97
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.217337
Coefficient of variation (CV)0.73234597
Kurtosis1.3599904
Mean23.509841
Median Absolute Deviation (MAD)11
Skewness1.0294021
Sum74056
Variance296.43671
MonotonicityNot monotonic
2025-03-15T21:58:25.915713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
2 88
 
2.8%
10 78
 
2.5%
15 77
 
2.4%
6 76
 
2.4%
17 76
 
2.4%
20 75
 
2.4%
19 75
 
2.4%
8 75
 
2.4%
16 74
 
2.3%
Other values (82) 2302
73.1%
ValueCountFrequency (%)
0 154
4.9%
1 31
 
1.0%
2 88
2.8%
3 44
 
1.4%
4 63
2.0%
5 60
 
1.9%
6 76
2.4%
7 61
 
1.9%
8 75
2.4%
9 73
2.3%
ValueCountFrequency (%)
97 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%
86 3
 
0.1%
85 3
 
0.1%
84 4
0.1%
83 4
0.1%
82 8
0.3%

Age Group
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3
1425 
2
1037 
4
395 
5
170 
1
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Length

2025-03-15T21:58:25.981125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T21:58:26.034741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring characters

ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Tariff Plan
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
1
2905 
2
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Length

2025-03-15T21:58:26.093839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T21:58:26.142654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring characters

ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Status
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
1
2368 
2
782 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Length

2025-03-15T21:58:26.193294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T21:58:26.242795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring characters

ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Customer Value
Real number (ℝ)

High correlation  Zeros 

Distinct2654
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.97292
Minimum0
Maximum2165.28
Zeros132
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-15T21:58:26.301799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.335
Q1113.80125
median228.48
Q3788.38875
95-th percentile1587.68
Maximum2165.28
Range2165.28
Interquartile range (IQR)674.5875

Descriptive statistics

Standard deviation517.01543
Coefficient of variation (CV)1.0977604
Kurtosis1.2244965
Mean470.97292
Median Absolute Deviation (MAD)160.5825
Skewness1.4272916
Sum1483564.7
Variance267304.96
MonotonicityNot monotonic
2025-03-15T21:58:26.372529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
4.2%
45.495 11
 
0.3%
40.44 10
 
0.3%
15.165 6
 
0.2%
25.275 5
 
0.2%
121.4 4
 
0.1%
180 4
 
0.1%
1538.145 4
 
0.1%
131.4 4
 
0.1%
197.64 3
 
0.1%
Other values (2644) 2967
94.2%
ValueCountFrequency (%)
0 132
4.2%
2.34 1
 
< 0.1%
4 1
 
< 0.1%
4.41 1
 
< 0.1%
4.5 2
 
0.1%
5.13 1
 
< 0.1%
5.175 1
 
< 0.1%
5.4 3
 
0.1%
5.625 1
 
< 0.1%
5.94 1
 
< 0.1%
ValueCountFrequency (%)
2165.28 1
< 0.1%
2149.28 1
< 0.1%
2148.84 1
< 0.1%
2148.03 1
< 0.1%
2140.96 1
< 0.1%
2129.535 1
< 0.1%
2127.68 1
< 0.1%
2124.84 1
< 0.1%
2120.67 1
< 0.1%
2117.72 1
< 0.1%

Churn
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.7 KiB
0
2655 
1
495 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Length

2025-03-15T21:58:26.436108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-15T21:58:26.484800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Interactions

2025-03-15T21:58:24.283873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.200409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.678799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.069245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.475492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.913706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.321512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.875790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.337205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.273092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.730629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.123982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.533814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.972490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.373523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.928881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.382915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.332142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.777191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.170210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.584827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.020227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.572795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.975872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.430653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.399986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.825805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.218572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.639492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.071703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.622254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.027113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.485217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.474290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.877753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.273464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.695875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.125829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.677412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.082006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.533133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.525484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.926199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.323036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.752425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.174505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.726888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.133443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.582121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.576732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.974996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.374779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.806432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.224517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.776513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.184558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.631881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:21.629072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.023897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.424764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:22.861756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.273813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:23.826959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-03-15T21:58:24.234770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-03-15T21:58:26.525961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Age GroupCall FailureCharge AmountChurnComplainsCustomer ValueDistinct Called NumbersFrequency of SMSFrequency of useSeconds of UseStatusSubscription LengthTariff Plan
Age Group1.0000.1030.2360.1320.0690.2140.2410.1730.2350.2970.1970.1620.192
Call Failure0.1031.0000.5720.0350.1700.3460.5140.2700.5500.4660.1210.2490.224
Charge Amount0.2360.5721.0000.1760.0630.3930.4370.3190.4470.4900.3210.1500.367
Churn0.1320.0350.1761.0000.5300.3180.2960.2530.3380.3530.4980.2170.103
Complains0.0690.1700.0630.5301.0000.1430.0780.1250.1430.1580.2690.1370.000
Customer Value0.2140.3460.3930.3180.1431.0000.5630.7800.6730.7140.4980.1450.434
Distinct Called Numbers0.2410.5140.4370.2960.0780.5631.0000.3210.8240.7630.4470.1570.219
Frequency of SMS0.1730.2700.3190.2530.1250.7800.3211.0000.3060.3080.3440.1040.452
Frequency of use0.2350.5500.4470.3380.1430.6730.8240.3061.0000.9370.5360.1740.422
Seconds of Use0.2970.4660.4900.3530.1580.7140.7630.3080.9371.0000.5960.1450.312
Status0.1970.1210.3210.4980.2690.4980.4470.3440.5360.5961.0000.1930.162
Subscription Length0.1620.2490.1500.2170.1370.1450.1570.1040.1740.1450.1931.0000.230
Tariff Plan0.1920.2240.3670.1030.0000.4340.2190.4520.4220.3120.1620.2301.000

Missing values

2025-03-15T21:58:24.702713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-15T21:58:24.810708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Call FailureComplainsSubscription LengthCharge AmountSeconds of UseFrequency of useFrequency of SMSDistinct Called NumbersAge GroupTariff PlanStatusCustomer ValueChurn
080380437071517311197.6400
10039031857421246.0350
2100370245360359243111536.5200
3100380419866135111240.0200
430380239358233111145.8050
51103813775823228311282.2800
640380236039285183111235.9600
7130372911512114443311945.4400
87038013773169044311557.6800
970381451583225311191.9200
Call FailureComplainsSubscription LengthCharge AmountSeconds of UseFrequency of useFrequency of SMSDistinct Called NumbersAge GroupTariff PlanStatusCustomer ValueChurn
3140160290100531179312109.440
31415028011301628541298.650
31421502711530382615211187.560
3143702713530671525311203.880
3144702012000323516311221.280
314521019266971479244221721.980
314617017192371778042511261.210
31471301843157513821311280.320
314870112469546222123111077.640
31498111217922579311100.681

Duplicate rows

Most frequently occurring

Call FailureComplainsSubscription LengthCharge AmountSeconds of UseFrequency of useFrequency of SMSDistinct Called NumbersAge GroupTariff PlanStatusCustomer ValueChurn# duplicates
290035000002120.00016
420037000002120.00006
240034000005110.00005
430037000002120.00015
220034000002120.00014
280035000002120.00004
330036000002120.00004
855039030567221245.49514
100704085342088311996.76003
2001601390202112211157.95003